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About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: The Safeguards ML Sampling Path team builds and operates the production services that power Claude's safety systems. These services sit on the token generation path across every platform Claude runs on: every request must pass through them, and each millisecond of added latency is wait time for our users. You’ll keep p99 latency flat as traffic grows, build for robustness as dependencies time out or partially fail, and ship changes safely to a system that cannot go down. What you'll do: • Design, build, and operate the backend systems that process every token on the generation path for Claude requests, including the streaming contract with the API and inference engines. • Own latency and reliability end to end: define and maintain SLOs and error budgets for added latency, time-to-first-token, and availability, and lead incident response and postmortem follow-through. • Ship changes to the hot path rapidly but safely — canaried and gradual rollouts, error budget and latency gating, fast rollbacks — and drive per-token performance: chase tail latency and keep cost flat as traffic, models, and checks per request grow. • Set technical direction for the sampling path: lead design reviews, make latency, reliability, and cost trade-off calls with the inference and research teams, mentor engineers, and raise the operational bar for the wider Safeguards organization. You may be a good fit if you: • Have designed, built, and operated high QPS systems at global scale, and were accountable for them in production: incident response, outages, and postmortem-driven remediation. • Have a strong foundation in distributed systems: replication, consistency tradeoffs, failure modes, and SLO management under load. • Design systems for graceful degradation: you plan for a slow dependency, a dropped stream, or a half-rolled-out deploy before it happens, and build so the system degrades predictably instead of failing. • Have successfully shipped broad or all-encompassing changes to mission critical systems (e.g., database migrations, interface changes, rewrites). Strong candidates may also have: • 8+ years of industry software engineering experience. • Familiarity with LLM inference systems and transformer-based models (not required, but a plus). The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we e

Staff+ Software Engineer, ML Sampling Path

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